1 Introduction

1.1 Background

This document provides a detailed summary of remote emission sensing (RES) data as part of the CARES project.

1.2 Aims

The analysis of RES data can be challenging given the complexity and typical size of the data collected during experimental campaigns. Even within the small community of researchers and practitioners that typically conduct experiments, there is a wide variation in the analysis approaches used and their consistency. With that in mind, this document has the following aims:

  1. To provide a reliable and automated way of presenting key summary data and plots from RES campaigns.

  2. Adopt ‘modern’ data analysis approaches using R Statistical Software and automated report production using Rmarkdown.

  3. These approaches offer many advantages over traditional ways of analysing data and presenting it. For example, allowing for detailed data to be presented in a compact way that can easily be filtered by the user, and the use of ‘tabs’ to better structure the output.

  4. To present common numerical and graphical outputs that help to interpret data from RES campaigns.

The analysis software and the underlying code that produced this document are part of a R package called openCARES. The package is available as a GitHub repository and all code is managed under a version control system. The approach means that all changes are recorded and that members of the CARES team can work collaboratively to develop the analysis capabilities over time.

This document is based on data collected using the EDAR as part of the Milan city demonstration campaign. The data set consists of approximately 35,500 measurements.

2 Vehicle numbers

This section focuses on the share of measurements per vehicle class, fuel type, Euro standard and so on.

2.1 Vehicle and fuel type

An example of a way in which to present vehicles samples is shown in Figure 2.1.

Figure 2.1: Numbers of vehicle by main vehicle and fuel type. Click on the key to select specific segments.

2.2 Euro standard

Euro standard share by main vehicle and fuel type.

Figure 2.2: Euro standard share by main vehicle and fuel type.

2.3 Manufacturers

Manufacturer composition for petrol and diesel vehicles. The size of each rectangle is proportional to the share of each manufacturer / manufacturer group.

2.3.1 Petrol vehicles

2.3.2 Diesel vehicles

3 Site conditions

This section could include site variables such as road grade, altitude, lat/long (consistent with the RES best practices document).

4 Environmental conditions

4.1 Meteorological data

4.1.1 All data

Pressure and relative humidity data will be included here.

Ambient temperature (deg C) n
22.02 35.57K
Density plot of all ambient temperature data.

Figure 4.1: Density plot of all ambient temperature data.

4.1.2 By site

Site Name Ambient temperature (deg C) n
Cilea 22.77 11.75K
Madre Cabrini 21.45 15.57K
Density plots of ambient temperature data, split by site.

Figure 4.2: Density plots of ambient temperature data, split by site.

4.2 Emissions as a function of ambient temperature

Fuel-specific emissions as a function of ambient temperature are shown below for CO, NOx and hydrocarbons. The relationships are categorised according to vehicle and fuel type.

4.2.1 CO

4.2.2 NOx

4.2.3 HC

5 Vehicle emissions

5.1 Emissions by Euro standard

5.1.1 CO

5.1.2 NOx

5.1.3 HC

5.2 Emissions by registration date

5.2.1 CO

5.2.2 NOx

5.2.3 HC

5.3 Emissions by manufacturer

5.3.1 PC gasoline

5.3.1.1 CO

5.3.1.2 NOx

5.3.1.3 HC

5.3.2 PC diesel

5.3.2.1 CO

5.3.2.2 NOx

5.3.2.3 HC

5.4 Emissions by engine size

5.5 Emission summaries

All data presented as fuel-specific emission factors i.e. g pollutant per kg fuel.

5.5.1 All data

nox no co hc n
4.54 3.77 10.68 1.97 35.57K

5.5.2 By fuel

fuel_type_1 nox no co hc n
CNG 5.65 5.18 11.95 3.41 178
diesel 7.69 6.09 2.69 1.45 14154
electricity NaN NaN NaN NaN 192
LPG 0.78 0.92 0.44 −7.48 1
petrol 2.29 2.11 16.45 2.29 20286
NA 5.01 3.99 7.00 2.79 757

5.6 Detailed pollutant summaries

5.6.1 NOx

The table below shows the mean and 95% confidence interval in the mean is given.

5.6.2 CO

The table below shows the mean and 95% confidence interval in the mean is given.

5.6.3 HC

The table below shows the mean and 95% confidence interval in the mean is given.

5.6.4 PM

The table below shows the mean and 95% confidence interval in the mean is given.

6 Vehicle dynamics

Speed, acceleration, VSP.

6.1 Measurement conditions

6.1.1 All data

Speed (kph) VSP n
32.88 4.57 27.32K
Density plot of all VSP data.

Figure 6.1: Density plot of all VSP data.

6.1.2 By site

site_name Speed (kph) VSP n
Cilea 42.64 4.83 11.75K
Madre Cabrini 25.51 4.38 15.57K
Density plot of all VSP data, split by site.

Figure 6.2: Density plot of all VSP data, split by site.

7 Deterioration effects

7.1 Emissions by vehicle mileage

Vehicle mileage data from annual technical inspection tests may be available in some cases. This is considered a good proxy for examining the effect of vehicle deterioration on emissions behaviour since it is a direct measure of the distance a vehicle has driven. Examples of the relationship between emissions and vehicle mileage are shown below for CO, NOx and hydrocarbons. This provides useful insight into the effect of vehicle ageing or deterioration on emissions behaviour.

7.1.1 CO

7.1.2 NOx

7.1.3 HC

7.2 Emissions by vehicle age

Useful proxy when vehicle mileage data not available.

8 Distance specific emissions

See conox_gkm.R